SlothSort

SlothSort: Let Your Files Sort Themselves

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

SlothSort is a calm, local-first file tidier for the Downloads folder you keep avoiding.

It proposes sensible destinations, better filenames, and possible duplicate warnings for messy files. Instead of spending an afternoon sorting screenshots, PDFs, documents, voice notes, and receipts, you review one simple plan.

SlothSort is designed around a very lazy workflow:

1. Add files.
2. Let the local sorter make a plan.
3. Review the suggestions.
4. Approve the tidy-up.

Nothing is silently deleted. Files marked as possible duplicates are flagged for review instead of being removed automatically.

The people who get to be lazier are freelancers, students, researchers, and anyone with a Downloads folder that has become a second desktop.

Demo

Live demo: SlothSort

Try this flow:

1. Open the demo.
2. Review the sample files in the queue.
3. Click Tidy all.
4. See the proposed destinations and duplicate warnings.
5. Click Add files to add local file metadata to the review queue.
6. Click Why local? to see the privacy explanation.

Code

Public repository: github.com/anshulchikhale30-p/wildquest

SlothSort lives in the public/slothsort/ directory.

How I Built It

SlothSort is a lightweight static web app with a small Node.js server. The interface is designed to feel calm instead of urgent, with a slow-and-steady workflow, transparent actions, and no destructive automation.

The project is built around a local AI architecture. A production version can send file metadata and a bounded sorting prompt to an Ollama-compatible endpoint running an open-weight model such as Qwen2.5 3B Instruct or Llama 3.2 3B.

The local model can suggest:

• A destination folder
• A cleaner filename
• Whether a file looks like a duplicate
• Whether an item needs human review

The demo includes a deterministic local fallback so the experience works without internet access. It uses transparent rules to classify common file types and create proposed destinations.

The important product decision is that SlothSort creates a plan before taking action. It does not silently delete files or move anything without approval.

Why Does Open Innovation Matter?

File organization often involves personal documents, work files, screenshots, receipts, and private media. Sending that entire folder to a hosted AI API is convenient, but it creates privacy concerns and a recurring cost.

Open and local AI makes a better version of this small tool possible:

• Files can be organized on a laptop without an internet connection.
• Personal file metadata does not need to leave the device.
• Users can swap Qwen, Llama, or another compatible open-weight model.
• The sorting prompt and decision rules remain inspectable.
• There is no per-file API bill.
• The user can change the agent’s behavior instead of accepting a black-box automation.

A closed API might make the first prototype faster, but the local approach is better for a personal file tool because the files are private, the task is repetitive, and the automation should be cheap enough to run whenever needed.

My Agent Session

I did not include a DevRelay embed. The implementation is available in the public repository.

The Lazy Payoff

The demo estimates approximately two minutes of manual effort saved per file, depending on how messy the folder is.

For an inbox of eight files, that is roughly 18 minutes of avoided sorting work. More importantly, SlothSort removes the hardest part: deciding where to begin.

I have not yet measured this with a paying user. The first people I would test it with are freelancers and students who regularly postpone organizing their Downloads folder because the task feels too small to schedule and too annoying to start.